Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles

Abstract: Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In addition, there is a growing need for complex scenarios with changing vehicle interactions for comprehensive validation. This work introduces a novel synchronous multi-agent simulation framework for autonomous vehicles in interactive scenarios. Our approach creates an interactive scenario and incorporates publicly available edge-case scenarios wherein simulated vehicles are replaced by agents navigating to predefined destinations. We provide a platform that enables the integration of different autonomous driving planning methodologies and includes a set of evaluation metrics to assess autonomous driving behavior. Our study explores different planning setups and adjusts simulation complexity to test the framework's adaptability and performance. Results highlight the critical role of simulating vehicle interactions to enhance autonomous driving systems. Our setup offers unique insights for developing advanced algorithms for complex driving tasks to accelerate future investigations and developments in this field. The multi-agent simulation framework is available as open-source software: https://github.com/TUM-AVS/Frenetix-Motion-Planner

• The paper introduces a novel multi-agent simulation framework that replicates complex driving interactions for autonomous vehicles.
• It integrates trajectory planning algorithms with safety metrics like TTC, headway, and DCE to assess dynamic AV behavior.
• The framework employs multi-core processing for scalability, paving the way for advanced V2V communication and cooperative planning research.

Multi-Agent Simulation Framework for Autonomous Vehicle Interaction

This paper presents a multi-agent simulation framework designed to assess autonomous vehicle (AV) systems within complex interactive driving scenarios. The framework aims to address existing limitations in AV validation, emphasizing the necessity for simulations capturing the nuanced interplay between multiple vehicles in dynamic environments.

Background and Motivation

Current practices in AV testing predominantly rely on real-world data and basic simulation techniques. These approaches fall short in capturing the comprehensive range of scenarios AVs might encounter, particularly those involving complex interactions among multiple vehicles. Real-world demonstration of AV safety, as indicated by existing literature, is economically prohibitive due to the required vast distances to be traversed for statistical validity. As such, there is a compelling need for simulation frameworks capable of replicating multifaceted traffic interactions to facilitate the safe development and deployment of AV technologies.

Framework Overview

The proposed framework provides a synchronized, multi-agent simulation environment wherein autonomous agents replace scripted vehicles, allowing for real-time interaction and adaptive behavior in traffic scenarios. It leverages publicly accessible edge-case scenarios and offers flexibility in the number of intelligent agents within a simulation.

Key contributions include:

• An open-source, advanced simulation framework compatible with CommonRoad benchmark scenarios, allowing reproducible simulations of interactive driving.
• An interface for integrating various trajectory planning algorithms, supplemented with evaluation metrics for assessing vehicle behavior.
• Evaluation of the framework's adaptability to different planning setups and its computational efficiency when scaled to multiple agents.

Methodological Details

Within the framework, each agent operates on a locally perceived scenario, enabling it to compute its trajectories based on the observable environment and predictions of future vehicle movements. The simulations are time-discrete, and multi-core computations are employed to enhance the processing efficiency.

The performance evaluation comprises both agent-specific success metrics and comprehensive safety benchmarking measures, such as headway, time-to-collision (TTC), and distance of closest encounter (DCE). The curvilinear coordinate system adopted in the calculations allows for a more precise assessment of driving behavior, particularly in intricate or curved road segments.

Results and Implications

With a notable reduction in computation time through multi-core processing, the framework demonstrates its scalability and usefulness in studies necessitating large numbers of agents or complex interaction patterns. Through illustrative scenarios of lane merging and intersection navigation, the paper highlights how different levels of interactivity influence AV behavior and safety metrics.

The insights gained underline the centrality of interactive simulation in improving AV trajectory planning methodologies. This also suggests new avenues for research into vehicle-to-vehicle (V2V) communication and cooperative autonomous systems.

Concluding Remarks

This simulation framework marks a significant step toward developing robust, interactive environments necessary for the safe evolution of AV systems. Future work can expand on cooperative planning models and incorporate varied planning methods within the framework, potentially utilizing reinforcement learning to further refine autonomous decision-making capabilities. The open-source nature of the framework invites continued community contributions, fostering an ecosystem of shared learning and collaborative advancement in autonomous vehicle research.